| """ |
| STER — Cross-LoD & Zero-shot difficulty map (run after the multi-LoD crawl). |
| |
| Uses the official ObjectPropertiesProcessor to compute the same 25 properties for |
| each (building, LoD), then builds two HARD tasks the clean Hague benchmark can't: |
| |
| TASK A Cross-LoD matching: |
| candidate = LoD1.2 (coarse block) , index = LoD2.2 (detailed roof) |
| positive = same BAG id ; negative = different id (blocking-style top-k) |
| -> baseline F1 expected << 0.95 (large geometric gap = genuine 'noisy' regime) |
| |
| TASK B Zero-shot transfer: |
| train a matcher on 3DBAG multi-LoD pairs (LoD1.2<->LoD2.2), |
| test on the Hague cross-source task (cand<->index) with NO target labels |
| -> measures whether transformation-consistency transfers across sources. |
| |
| Outputs experiments/crosslod/crosslod_zeroshot.json |
| """ |
| import os, sys, json, warnings, numpy as np, joblib |
| warnings.filterwarnings("ignore") |
| sys.path.insert(0, ".") |
| import config |
| config.Features.normalization = 'log_transform' |
| from object_properties import ObjectPropertiesProcessor |
| from sklearn.ensemble import BaggingClassifier |
| from sklearn.metrics import f1_score, precision_score, recall_score |
|
|
| ML_DIR = "../../data/3dbag_multilod" |
| PROPS = config.Features.object_properties |
| MAX_RATIO = config.Constants.max_ratio_val |
| SEED = 1 |
| rng = np.random.RandomState(SEED) |
|
|
| if not os.path.exists(os.path.join(ML_DIR, "3dbag_lod22.joblib")): |
| print("multi-LoD data not present yet:", ML_DIR); sys.exit(0) |
|
|
| lod12 = joblib.load(os.path.join(ML_DIR, "3dbag_lod12.joblib")) |
| lod22 = joblib.load(os.path.join(ML_DIR, "3dbag_lod22.joblib")) |
| common = sorted(set(lod12) & set(lod22)) |
| print(f"multi-LoD buildings: lod12={len(lod12)} lod22={len(lod22)} common={len(common)}", flush=True) |
|
|
|
|
| def props_for(object_dict): |
| """Compute the 25 log-normalised properties -> {prop:{'cands':{id:v},'index':{id:v}}}.""" |
| p = ObjectPropertiesProcessor(object_dict, vector_normalization=True) |
| return p.prop_vals_dict |
|
|
|
|
| def ratio_feat(pd, c, i, cand_side='cands', idx_side='index'): |
| row = [] |
| for p in PROPS: |
| try: |
| cv, iv = pd[p][cand_side][c], pd[p][idx_side][i] |
| row.append(min(MAX_RATIO, round(cv / iv, 3)) if iv != 0 else MAX_RATIO) |
| except (KeyError, ZeroDivisionError): |
| row.append(0.0) |
| return row |
|
|
|
|
| def build_pairs(ids_cand, ids_idx, k_neg=2): |
| """positive: (id,id); negatives: k_neg random different-id index buildings.""" |
| pairs, labels = [], [] |
| idx_pool = list(ids_idx) |
| for c in ids_cand: |
| if c in ids_idx: |
| pairs.append((c, c)); labels.append(1) |
| negs = rng.choice(idx_pool, size=min(k_neg, len(idx_pool)), replace=False) |
| for n in negs: |
| if n != c: |
| pairs.append((c, n)); labels.append(0) |
| return pairs, np.array(labels) |
|
|
|
|
| |
| print("\n[TASK A] Cross-LoD matching (LoD1.2 -> LoD2.2)", flush=True) |
| od = {'cands': {k: lod12[k] for k in common}, 'index': {k: lod22[k] for k in common}} |
| pd_cl = props_for(od) |
| pairs, y = build_pairs(common, set(common), k_neg=2) |
| X = np.array([ratio_feat(pd_cl, c, i) for c, i in pairs]) |
| |
| tr_ids = set(rng.choice(common, int(0.6 * len(common)), replace=False)) |
| tr = np.array([1 if p[0] in tr_ids else 0 for p in pairs], dtype=bool) |
| clf = BaggingClassifier(n_estimators=50, random_state=SEED).fit(X[tr], y[tr]) |
| pred = clf.predict(X[~tr]) |
| taskA = dict(n_pairs=len(pairs), n_pos=int(y.sum()), |
| precision=round(precision_score(y[~tr], pred, zero_division=0), 4), |
| recall=round(recall_score(y[~tr], pred, zero_division=0), 4), |
| f1=round(f1_score(y[~tr], pred, zero_division=0), 4)) |
| print(" ", taskA, flush=True) |
|
|
| report = {"n_common": len(common), "task_A_cross_lod": taskA} |
| os.makedirs("../../experiments/crosslod", exist_ok=True) |
| with open("../../experiments/crosslod/crosslod_zeroshot.json", "w") as f: |
| json.dump(report, f, indent=2) |
| print("\nSaved -> experiments/crosslod/crosslod_zeroshot.json", flush=True) |
| print("NOTE: Task B (zero-shot transfer to Hague) added once Hague props are aligned.", flush=True) |
|
|
